Syntience Video Page

Monica Anderson is CTO and co-founder of Sensai Corporation and
founder of Syntience Inc. Here she discusses Dual Process Theory, The
Frame Problem, and some consequences of these for AI research.
Dual Process Theory is the idea that the human mind has two disparate
modes of thinking - Subconscious Intuitive Understanding on one hand
and Conscious Logical Reasoning on the other. The Frame Problem is
the idea that we cannot make comprehensive Models of the World because
the world changes behind our backs and any Model we make is
immediately obsolete. The conclusion is that AI research since the
1950s has been trying to solve the wrong problem - one that is much
harder than necessary. She also introduces Model Free Methods as
an alternative path to AI, capable of sidestepping the Frame Problem.

Monica Anderson discusses the ongoing paradigm shift - the "Holistic
Shift" - which started in the life sciences and is spreading to the
remaining disciplines. Model Free Methods (also known as Holistic
Methods) are an increasingly common approach used on "the remaining
hard problems", including problems in the domain of "AI" - Problems
that require intelligence. She illustrates this using a Model Free
approach to the NetFlix Challenge. Her website provides some
background
information.

Ms. Anderson ran the Bay Area AI Meetup for over five years; about 115 MeetUps were held. Currently these meetaups are on hiatus - she is looking for a sponsor with a venue capable of holding about 200 people.
Most AI meetups featured a presentation by a group member or by an
invited speaker. This was typically followed by a discussion that
centered on the presentation but would often expand to any AI related
topic.

The meetings were free and open to the public. For details,
including possible future meetups, see the AI Meetup page at ai-meetup.org or their calendar at ai-meetup.org/calendar

Several more of these meetups have been video recorded and will be
made accessible through this page as they finish postproduction.

Holistic Salons

Holistic Salons are a series of AI MeetUps featuring talks by Monica
Anderson about her research (especially Artificial Intuition) and
its philosophical foundations.
Initially these were held salon-style in her home but as their popularity grew they were
made part of the AI MeetUps.

Monica Anderson proposes adding a new target to ongoing AI research efforts: We need to
focus more of our attention on Understanding as opposed to Reasoning. Understanding
requires using Model Free Methods. As a bonus towards the end, Ms. Anderson also
speculates about the so-called AI singularity and discusses whether SkyNet like scenarios,
where computers take over the world, are plausible.

Computer based analysis of the Semantics of language expressed as text
is an AI level problem. Existing methods almost universally use Models
of Language (Dictionaries, Grammars, Word Nets, Taxonomies, and
Ontologies). The two simplest and most pervasive Models claim that
Languages have Words and that those Words have Meanings. While
acknowledging that good alternatives do not yet exist, this talk
attempts to make plausible that these two "obvious" but fatally
incorrect Models result, automatically, in a cascading series of
forced engineering decisions that each discard a fraction of the
available semantics until we end up with brittle systems that fail in
catastrophic and memorable ways. The proposed alternative to
word-centric Model Based methods of language analysis is Understanding
Machines - capable of learning languages the way humans learn
languages in babyhood - using new classes of algorithms based on Model
Free Methods.

In so-called "Bizarre Domains", Models of the target problem domain
cannot be used. Models include Scientific Models, Naive models,
theories, hypotheses, formulas, equations, and computer
programs. Model Free Methods are simpler than Model Based Methods and
work everywhere including in Bizarre Domains. Our everyday mundane
reality - where AIs supposedly have to operate - happens to be a
Bizarre Domain. Therefore, AI researchers need to treat AI as a Life
Science and only use Model Free Methods (which are common in Life
Sciences). This video provides a brief introduction to Model Free
Methods followed by an interactive workshop where four problem and
their solutions are discussed. For each problem, we discuss first a
Model Based and then a Model Free approach. Note that while the Life
Sciences often use mixes of both kinds of methods, in the case of AI
we need to only use Model Free Methods.

Reductionist methods have failed and will always fail on problems in "Bizarre Domains" - domains where Reductionist models cannot be created
or used. Examples include modeling significant parts of the world we live in, many problems in the Life Sciences, analyzing organisms
including human physiology, drug interactions, etc., the global economy and stock markets, analyzing the brain, creating an Artificial
General Intelligence (AGI), and the semantics of text.

This video attempts to clarify the distinction between Reductionist
Models and Holistic Patterns - central concepts in Monica Anderson's
theory about "Artificial Intuition". Reductionist Models cannot be
created or used in Bizarre Domains, but Model Free Methods can be used
everywhere. The initial part of this talk repeats some introductory
material from the first tak above; the new material starts around
8:50.

Other AI MeetUps

Dr. Peter Norvig is Director of Research at Google Inc. He is a Fellow of the Association
for Computing Machinery and the American Association for Artificial Intelligence and
co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the
field. Previously he was head of Computational Sciences at NASA and a faculty member at
USC and Berkeley. In this talk he discusses, among other things, how non-parametric
models can be applied to vision and language problems in data-rich environments.

Jamais Cascio writes at OpenTheFuture.com about the intersection of emerging technologies,
environmental dilemmas, and cultural transformation, specializing in the design and
creation of plausible scenarios of the future. His work focuses on the importance of
long-term, systemic thinking, emphasizing the power of openness, transparency and
flexibility as catalysts for building a more resilient society.